This study identifies the predicted seismic expression of the high-to-low iron spin crossover in the deep Earth mineral ferropericlase. A depth-dependent signal is detected in the fastest and slowest regions, related to lateral temperature variations, of several global seismic tomography models.
(2021). Nuclear Technology: Vol. 207 No. 8 pp. 1163–1181.
Focusing on nuclear engineering applications, the nation’s leading cybersecurity programs are focused on developing digital solutions to support reactor control for both on-site and remote operation. Many of the advanced reactor technologies currently under development by the nuclear industry, such as small modular reactors, microreactors, etc., require secure architectures for instrumentation, control, modeling, and simulation in order to meet their goals. 1 Thus, there is a strong need to develop communication solutions to enable secure function of advanced control strategies and to allow for an expanded use of data for operational decision making. This is important not only to avoid malicious attack scenarios focused on inflicting physical damage but also covert attacks designed to introduce minor process manipulation for economic gain. 2
These high-level goals necessitate many important functionalities, e.g., developing measures of trustworthiness of the code and simulation results against unauthorized access; developing measures of scientific confidence in the simulation results by carefully propagating and identifying dominant sources of uncertainties and by early detection of software crashes; and developing strategies to minimize the computational resources in terms of memory usage, storage requirements, and CPU time. By introducing these functionalities, the computers are subservient to the programmers. The existing predictive modeling philosophy has generally been reliant on the ability of the programmer to detect intrusion via specific instructions to tell the computer how to detect intrusion, keep log files to track code changes, limit access via perimeter defenses to ensure no unauthorized access, etc.
The last decade has witnessed a huge and impressive development of artificial intelligence (AI) algorithms in many scientific disciplines, which have promoted many computational scientists to explore how they can be embedded into predictive modeling applications. The reality, however, is that AI, premised since its inception on emulating human intelligence, is still very far from realizing its goal. Any human-emulating intelligence must be able to achieve two key tasks: the ability to store experiences and the ability to recall and process these experiences at will. Many of the existing AI advances have primarily focused on the latter goal and have accomplished efficient and intelligent data processing. Researchers on adversarial AI have shown over the past decade that any AI technique could be misled if presented with the wrong data. 3 Hence, this paper focuses on introducing a novel predictive paradigm, referred to as covert cognizance, or C2 for short, designed to enable predictive models to develop a secure incorruptible memory of their execution, representing the first key requirement for a human-emulating intelligence. This memory, or self-cognizance, is key for a predictive model to be effective and resilient in both adversarial and nonadversarial settings. In our context, “memory” does not imply the dynamic or static memory allocated for a software execution; instead, it is a collective record of all its execution characteristics, including run-time information, the output generated in each run, the local variables rendered by each subroutine, etc.
(2021). Nuclear Science and Engineering: Vol. 195 No. 9 pp. 977–989.
Earlier work has demonstrated the theoretical development of covert OT defenses and their application to representative control problems in a nuclear reactor. Given their ability to store information in the system nonobservable space using one-time-pad randomization techniques, the new C2 modeling paradigm6 has emerged allowing the system to build memory or self-awareness about its past and current state. The idea is to store information using randomized mathematical operators about one system subcomponent, e.g., the reactor core inlet and exit temperature, into the nonobservable space of another subcomponent, e.g., the water level in a steam generator, creating an incorruptible record of the system state. If the attackers attempt to falsify the sensor data in an attempt to send the system along an undesirable trajectory, they will have to learn all the inserted signatures across the various system subcomponents and the C2 embedding process.
We posit that this is extremely unlikely given the huge size of the nonobservable space for most complex systems, and the use of randomized techniques for signature insertion, rendering a level of security that matches the Vernam-Cipher gold standard. The Vernam Cipher, commonly known as a one-time pad, is a cipher that encrypts a message using a random key (pad) and can only be decrypted using this key. Its strength is derived from Shannon’s notion of perfect secrecy 8 and requires the key to be truly random and nonreusable (one time). To demonstrate this, this paper will validate the implementation of C2 using sophisticated AI tools such as long short-term memory (LSTM) neural networks 9 and the generative adversarial learning [generative adversarial networks (GANs)] framework, 10 both using a supervised learning setting, i.e., by assuming that the AI training phase can distinguish between original data and the data containing the embedded signatures. While this is an unlikely scenario, it is assumed to demonstrate the resilience of the C2 signatures to discovery by AI techniques.
The paper is organized as follows. Section II provides a brief summary of existing passive and active OT defenses against various types of data deception attacks, followed by an overview of the C2 modeling paradigm in Sec. III. Section IV formulates the problem statement of the C2 implementation in a generalized control system and identifies the key criteria of zero impact and zero observability. Section V implements a rendition of the C2 approach in a representative nuclear reactor model and highlights the goal of the paper, i.e., to validate the implementation using sophisticated AI tools. It also provides a rationale behind the chosen AI framework. Last, Sec. VI summarizes the validation results of the C2 implementation and discusses several extensions to the work.
The Zwicky Transient Facility recently announced the detection of an optical transient AT2020blt at redshift $z=2.9$, consistent with the afterglow of a gamma-ray burst. No prompt emission was observed. We analyse AT2020blt with detailed models, showing the data are best explained as the afterglow of an on-axis long gamma-ray burst, ruling out other hypotheses such as a cocoon and a low-Lorentz factor jet. We search \textit{Fermi} data for prompt emission, setting deeper upper limits on the prompt emission than in the original detection paper. Together with \konus{} observations, we show that the gamma-ray efficiency of AT2020blt is $\lesssim 2.8\%$, lower than $98.4\%$ of observed gamma-ray bursts. We speculate that AT2020blt and AT2021any belong to the low-efficiency tail of long gamma-ray burst distributions that are beginning to be readily observed due to the capabilities of new observatories like the Zwicky Transient Facility.
Interview with a very important researcher who gives a reality check on a few things (Sirtuins) and explains how effective Rapamycin has been.
Professor Matt Kaeberlein discusses aspects of aging and proposed interventions to improve health. He gives an in-depth review on sirtuins, resveratrol, fasting, NAD precursors, and rapamycin.
Timestamps.
SpaceX is prepping SN20 for the Starship program’s first orbital test flight.
SpaceX’s SN20 Starship prototype just took its first fiery breath.
SpaceX conducted a brief engine test at its South Texas facilities last night (Oct. 18) with SN20, which the company is prepping to make the Starship program’s first-ever orbital test flight.
The central principle of superconductivity is that electrons form pairs. But can they also condense into foursomes? Recent findings have suggested they can, and a physicist at KTH Royal Institute of Technology today published the first experimental evidence of this quadrupling effect and the mechanism by which this state of matter occurs.
Reporting in Nature Physics, Professor Egor Babaev and collaborators presented evidence of fermion quadrupling in a series of experimental measurements on the iron-based material, Ba1−xKxFe2As2. The results follow nearly 20 years after Babaev first predicted this kind of phenomenon, and eight years after he published a paper predicting that it could occur in the material.
The pairing of electrons enables the quantum state of superconductivity, a zero-resistance state of conductivity which is used in MRI scanners and quantum computing. It occurs within a material as a result of two electrons bonding rather than repelling each other, as they would in a vacuum. The phenomenon was first described in a theory by, Leon Cooper, John Bardeen and John Schrieffer, whose work was awarded the Nobel Prize in 1972.
Retail giant Amazon has pioneered the idea of automated shopping, as seen with its Amazon Go store format. The first of these launched in January 2018 in downtown Seattle and nearly 30 others have opened since. The concept is now catching on with other companies – including Tesco, the UK’s biggest supermarket and third-largest retailer in the world measured by gross revenues. It has just launched its own automated store in central London.
The rollout of this technology at Tesco Express High Holborn follows a successful trial in Welwyn Garden City, a town north of London. The High Holborn branch has already been a cashless store since it first opened in 2018 and is now checkout-less too.
The newly developed system – called “GetGo” – offers the same products but with a faster and more convenient shopping experience. A customer simply downloads the mobile app, scans the QR code generated on their screen, picks up the groceries they need and then leaves the store.
Chinese and Russian vessels have for the first time jointly traversed the Tsugaru Strait in between Honshu and Hokkaido islands.
SambaNova Systems, a company that builds advanced software, hardware, and services to run AI applications, announced the addition of the Generative Pre-trained Transformer (GPT) language model to its Dataflow-as-a-Service™ offering. This will enable greater enterprise adoption of AI, allowing organizations to launch their customized language model in much less time — less than one month, compared to nine months or a year.
“Customers face many challenges with implementing large language models, including the complexity and cost,” said R “Ray” Wang, founder and principal analyst of Constellation Research. “Leading companies seek to make AI more accessible by bringing unique large language model capabilities and automating out the need for expertise in ML models and infrastructure.”
